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Publication numberUS20050278051 A1
Publication typeApplication
Application numberUS 10/856,016
Publication dateDec 15, 2005
Filing dateMay 28, 2004
Priority dateMay 28, 2004
Also published asUS7096085, US7221990, US20060195214
Publication number10856016, 856016, US 2005/0278051 A1, US 2005/278051 A1, US 20050278051 A1, US 20050278051A1, US 2005278051 A1, US 2005278051A1, US-A1-20050278051, US-A1-2005278051, US2005/0278051A1, US2005/278051A1, US20050278051 A1, US20050278051A1, US2005278051 A1, US2005278051A1
InventorsYoung Paik
Original AssigneeApplied Materials, Inc.
Export CitationBiBTeX, EndNote, RefMan
External Links: USPTO, USPTO Assignment, Espacenet
Process control by distinguishing a white noise component of a process variance
US 20050278051 A1
Abstract
A method, system and medium is provided for enabling improved control systems. An error, or deviation from a target result, is observed for example during manufacture of semiconductor chips. The error within standard deviation is caused by two components: a white noise component and a signal component (such as systematic errors). The white noise component is, e.g., random noise and therefore is relatively non-controllable. The systematic error component, in contrast, may be controlled by changing the control parameters. A ratio between the two components is calculated autoregressively. Based on the ratio and using the observed or measured error, the actual value of the error caused by the systematic component is calculated utilizing an autoregressive stochastic sequence. The actual value of the error is then used in determining when and how to change the control parameters. The autoregressive stochastic sequence addresses the issue of the effects of run-to-run deviations, and provides a mechanism that can extract the white noise component from the statistical process variance in real time. This results in an ability to provide tighter control, for example in feedback and feedforward variations of process control.
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Claims(34)
1. A system, implemented on at least one computer, for compensating for a variance between a measured characteristic of at least one product produced by a process and a target result of the characteristic, the system comprising:
(a) means for receiving an observed value for at least one product, and receiving a target value for the at least one product;
(b) means for determining a variance between the observed value of at least one product and the target value of the at least one product;
(c) means for determining a first portion of the variance caused by white noise;
(d) means for determining a second portion of the variance caused by a systematic component; and
(e) means, in the at least one computer, for using the determined first and/or second portions to adjust the process.
2. The system of claim 1, wherein the at least one product is a semi-conductor wafer, and the process is an automated semiconductor manufacturing process.
3. The system of claim 1, wherein the target value is derived from a specification, the specification being selected from at least one of a predetermined specification, and a real-time calculation taken from a plurality of prior observed values of products.
4. The system of claim 1, wherein the process has at least one control parameter capable of being controlled, further comprising means for controlling the at least one control parameter during the process based on the second portion of the variance.
5. The system of claim 4, further comprising means for determining the observed value for a plurality of products including the at least one product; determining at least the second portion of the variance for the plurality of products and utilizing the second portion of the variance for the plurality of products as a threshold; and determining whether or not to control the plurality of products when the observed value is outside the threshold.
6. The system of claim 4, wherein the process includes at least one device on which the plurality of products including the at least one product is processed, the observed value being relative to the at least one device, the at least one device including the at least one control parameter, wherein controlling the at least one control parameter includes affecting the at least one device.
7. The system of claim 4, wherein the process includes a plurality of devices including a first device and a second device on which the plurality of products including the at least one product are processed, the observed value being relative to the first device, the second device including the at least one control parameter, wherein controlling the at least one control parameter includes affecting at least the second device.
8. The system of claim 1, wherein the first portion and second portion are determined over a plurality of products.
9. The system of claim 1, wherein the first portion and second portion of the variance are calculated using an autoregressive stochastic sequence.
10. The system of claim 1, wherein the first portion and second portion of the variance are substantially determined by:

δx =y/(1+z)
where
δx=a value representing the variation that a system can control
y=calculated value from previous values
z=estimated gain adjustment.
11. The system of claim 1, wherein the target value is derived from a specification, the specification being selected from at least one of a predetermined specification, and a real-time calculation taken from a plurality of prior observed values of products.
12. The system of claim 1, wherein the white noise includes at least one of random variance, normal deviation, and an ambient fluctuation.
13. A computer-implemented method for compensating for a variance between a measured characteristic of at least one product produced by a process and a target result of the characteristic, comprising the steps of:
(a) receiving an observed value for at least one product, and receiving a target value for the at least one product;
(b) determining a variance between the observed value of at least one product and the target value of the at least one product;
(c) determining a first portion of the variance caused by white noise;
(d) determining a second portion of the variance caused by a systematic component; and
(e) using the determined first and/or second portions to adjust the process.
14. The method of claim 13, wherein the at least one product is a semiconductor wafer, and the process is an automated semiconductor manufacturing process.
15. The method of claim 13, wherein the target value is derived from a specification, the specification being selected from at least one of a predetermined specification, and a real-time calculation taken from a plurality of prior observed values of products.
16. The method of claim 13, wherein the process has at least one control parameter capable of being controlled, further comprising the step of controlling the at least one control parameter during the process based on the second portion of the variance.
17. The method of claim 16, further comprising the steps of determining the observed value for a plurality of products including the at least one product; determining at least the second portion of the variance for the plurality of products and utilizing the second portion of the variance for the plurality of products as a threshold; and determining whether or not to perform the controlling step for the plurality of products when the observed value is outside the threshold.
18. The method of claim 16, wherein the process includes at least one device on which the plurality of products including the at least one product is processed, the observed value being relative to the at least one device, the at least one device including the at least one control parameter, wherein the step of controlling the at least one control parameter includes affecting the at least one device.
19. The method of claim 16, wherein the process includes a plurality of devices including a first device and a second device on which the plurality of products including the at least one product are processed, the observed value being relative to the first device, the second device including the at least one control parameter, wherein the step of controlling the at least one control parameter includes affecting at least the second device.
20. The method of claim 3, wherein the first portion and second portion are determined over a plurality of products.
21. The method of claim 13, wherein the first portion and second portion of the variance are calculated using an autoregressive stochastic sequence.
22. The method of claim 13, wherein the first portion and second portion of the variance are substantially determined by:

δx =y/(1+z)
where
δx=a value representing the variation that a system can control
y=calculated value from previous values
z=estimated gain adjustment.
23. The method of claim 13, wherein the white noise includes at least one of random variance, normal deviation, and an ambient fluctuation.
24. A computer program product for use in compensating for a variance between a measured characteristic of at least one product produced by a process and a target result of the characteristic by differentiating a white noise component of the variance from a systematic component of the variance, the computer program product comprising:
(a) at least one computer readable medium, readable by the manufacturing process;
(b) instructions, provided on the at least one computer readable medium, for receiving an observed value for at least one product, and receiving a target value for the at least one product;
(c) instructions, provided on the at least one computer readable medium, for determining a variance between the observed value of at least one product and the target value of the at least one product;
(d) instructions, provided on the at least one computer readable medium, for determining a first portion of the variance caused by white noise; and
(e) instructions, provided on the at least one computer readable medium, for determining a second portion of the variance caused by a systematic component; and
(f) instructions, provided on the at least one computer readable medium, for using the determined first and/or second portions to adjust the process.
25. The computer program product of claim 24, wherein the at least one product includes a semi-conductor wafer, and the computer readable medium is readable by a process including an automated semiconductor manufacturing process.
26. The computer program product of claim 24, wherein the target value is derived from a specification, the specification being selected from at least one of a predetermined specification, and a real-time calculation taken from a plurality of prior observed values of products.
27. The computer program product of claim 24, wherein the process has at least one control parameter capable of being controlled, further comprising instructions, provided on the computer readable medium, for controlling the at least one control parameter during the process based on the second portion of the variance.
28. The computer program product of claim 27, further comprising instructions, on the computer readable medium, for determining the observed value for the plurality of products including the at least one product; determining at least the second portion of the variance for the plurality of products and utilizing the second portion of the variance for the plurality of products as a threshold; and determining whether or not to execute the controlling instructions for the at least one product when the observed value is outside the threshold.
29. The computer program product of claim 28, wherein the process includes at least one device on which the plurality of products including the at least one product is processed, the observed value being relative to the at least one device, the at least one device including the at least one control parameter, wherein the instructions for controlling the at least one control parameter includes affecting the at least one device.
30. The computer program product of claim 28, wherein the process includes a plurality of devices including a first device and a second device on which the plurality of products including the at least one product are processed, the observed value being relative to the first device, the second device including the at least one control parameter, wherein the instructions for controlling the at least one control parameter includes affecting at least the second device.
31. The computer program product of claim 24, wherein the first portion and the second portion are determined over a plurality of products.
32. The computer program product of claim 24, wherein the first portion and second portion of the variance are calculated using an autoregressive stochastic sequence.
33. The computer program product of claim 24, wherein the first portion and second portion of the variance are substantially determined by:

δx =y/(1+z)
where
δx=a value representing the variation that a system can control
y=calculated value from previous values
z=estimated gain adjustment.
34. The computer program product of claim 24, wherein the white noise includes at least one of random variance, normal deviation, and an ambient fluctuation.
Description
BACKGROUND OF THE INVENTION CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to co-pending application entitled “Dynamic Offset and Feedback Threshold,” filed on even date herewith and incorporated by reference.

1. Field of the Invention

The present invention concerns computer-implemented and/or computer-enabled methods, systems, and mediums for enabling improved control of errors during process control. More specifically, one or more embodiments of the present invention relate to distinguishing a variance due to white noise from a statistical process variance measured during process control, particularly in connection with semiconductor manufacturing.

2. Description of the Related Art

Detecting and controlling errors during a manufacturing process is an important goal. This is particularly true in connection with the process of manufacturing semiconductors. During the manufacturing process, various measurements are made in order to detect and/or determine errors, e.g., to detect when an observed value differs significantly from an intended target result. When the difference is sufficient, the manufacturing process control system will attempt to control (e.g., compensate for) the error so as to continue to produce products (e.g., chips) that are within acceptable ranges or tolerances from the target result.

In general, it has been observed that semiconductor devices processed in connection with a semiconductor manufacturing process will inevitably include at least some error or some deviation from the intended target result or specification. In order to determine when it is desired to perform additional control in order to make an adjustment during processing, conventional systems utilize a threshold value as a trigger. Whenever the additional control is triggered and the adjustment is made, however, the result following the adjustment still will usually deviate from the intended target result. Further, there are tolerances within which a tighter adjustment of a control does not effectively cause the material to be processed closer to specification, since the control is simply not capable of a sufficiently fine adjustment.

Conventionally, control of an error is attempted when one or more preconditions assigned to a tolerance range for the target specification, are evaluated using a statistical approach and are satisfied. The conventional statistical approach employs a standard deviation. Nevertheless, even when the process control system uses standard deviation as the threshold value, there is always a lack of precision, or a tolerance range within which it is not truly possible to control more tightly.

A significant reason often preventing tolerance ranges from being controlled more tightly is that some portion of the measurement, including the amount of observed value which is determined to be error, may actually be due to “white noise”. White noise does not necessarily represent an “error”. White noise represents an uncontrollable (and typically temporary) fluctuation in measurement. One example of white noise in a semiconductor manufacturing system is a sudden and temporary disturbance in ambient temperature, which is neither measured nor controlled, but would result in a change in thickness of the product. When determining whether an error occurred and/or how much (and whether) to compensate for any difference from the desired target result and/or to control an error that may occur during the manufacturing process, white noise should be taken into account.

The true amount of white noise occurring during manufacturing processes is not easily determined. In order to accommodate or adjust for white noise, the semiconductor industry conventionally utilizes a statistical process variance, or standard deviation, determined at optimal conditions, as an estimation of white noise. Thus, statistical process variance that is used as a substitute for what would otherwise be a truer (e.g., more real world) measurement of a general type of white noise is measured while the process, material to be processed, and processing device conditions are at an atypically pristine state. This type of measurement of white noise at pristine conditions yields a measurement during a best looking steady state performance, reflecting what cannot be controlled even at the best of conditions. The measurement at these atypical conditions is then utilized as an estimation of the white noise occurring throughout the manufacturing process under consideration.

The problem with the aforementioned conventional use of measurements at pristine conditions as a white noise estimate, despite its industry acceptance, is that it is not a reasonably accurate reflection of white noise that occurs during real manufacturing conditions. One of many reasons that measurements at pristine conditions do not reasonably reflect true conditions is that materials such as wafers processed in most front and back end processing devices in the semiconductor industry have relationships with or effects on subsequently processed wafers. Accordingly, for example, conditions applied to wafers that were previously processed in a processing device will have residual effects on wafers that are currently being processed in that processing device. An estimation of white noise derived from measurements taken while the processing device is at a steady state, consequently, does not reflect the fluctuations introduced during real-world run-to-run processing.

Regarding the aforementioned conventional techniques, statistical use of standard deviation in connection with observed deviation is illustrated, for example in “Statistical feedback control of a plasma etch process”, P. Mozumder et al., IEEE Transactions on Semiconductor Manufacturing, Vol. 7, No. 1 (February 1994) (incorporated herein by reference). The statistical variance Sk at the kth run is calculated using the standard deviation as: s k = 1 n - 1 [ i = 1 n X k - i + 1 2 - n ( X _ ) 2 ] ( 1 )

    • where
    • n=number of samples
    • X=deviation of observed value from predicted value

As can be seen, the standard deviation calculation does not distinguish between systematic variation and white noise variation components of the error. Hence, conventionally both the systematic and white noise variations are controlled together, rather than separately.

The conventional process control system thus compares this observed “combined” standard deviation to a threshold in order to determine if the deviation is unacceptable. Once the standard deviation greater than the threshold is detected, a tuning procedure in the process model is invoked in order to appropriately control the deviation. In essence, standard deviation-based methods only act to control when the standard deviation range is outside a particular threshold or trigger. In the conventional process control method, the standard deviation is used to determine the level for the threshold or trigger. Within the threshold, it is assumed that the deviation cannot be sufficiently controlled.

Therefore, there remains a need to have improved control, particularly within a tolerance range associated with a target specification. There also remains a need to address the effects of run-to-run conditions on such measurements.

BRIEF SUMMARY OF THE INVENTION

The present invention provides a way to determine the “error” within standard deviation which is caused by two components: a white noise component and a systematic component. The white noise component is random noise and therefore is relatively non-controllable. The systematic component, in contrast, may be controlled by changing the control parameters. A ratio between the two components is calculated autoregressively. Based on the ratio and using the observed or measured error (as, e.g., typically made over the course of several measurements), the actual value of the error caused by the systematic component is calculated utilizing an autoregressive stochastic sequence. The actual value of this error is then used in determining how to change the control parameters. The autoregressive stochastic sequence addresses the issue of real-time control of the effects of run-to-run deviations, and provides a mechanism that can extract the white noise component from the statistical process variance in real time. This results in an ability to provide tighter control of feedback and feedforward variations and according to one or more embodiments of the present inventions, may be used in conjunction with a dynamic adaptation of estimated gain, estimation of recursive parameters, and dynamic filtration in searching for individual optimum vectors for example.

One potential use of one or more embodiments of the present invention is to provide an estimated gain adjustment which provides optimal weight factors via an autoregressive stochastic sequence.

In accordance with one or more embodiments of the invention, there is provided a computer-implemented method, system and/or a computer program product, for compensating for a variance between a measured characteristic of at least one product produced by a process and a target result of the characteristic, by differentiating a white noise component of the variance from a systematic component of the variance. An observed value is received for at least one product, and a target value is received for the product. A variance is determined between the observed value of at least one product and the target value of the at least one product. A first portion of the variance caused by white noise is determined. A second portion of the variance caused by a systematic component is determined. The first portion and/or second portions are used to adjust the process.

According to one or more embodiments of the invention, the products are semiconductor wafers, and the manufacturing process is an automated semiconductor manufacturing process.

According to one or more embodiments of the invention, the target value is derived from a specification, the specification being selected from at least one of a predetermined specification, and a real-time calculation taken from a plurality of prior observed values of products.

According to one or more embodiments of the invention, the process has at least one control parameter capable of being controlled; and the at least one control parameter is controlled during a process based on the second portion of the variance.

According to one or more embodiments of the invention, the value for each product of the plurality of products including the at least one product is determined; at least the second portion of the variance for the plurality of products is determined and the second portion of the variance is utilized as a threshold; and it is determined whether or not to perform the controlling step for the plurality of products when the observed value is outside the threshold.

According to one or more embodiments of the invention, the process includes at least one device on which the plurality of products including the at least one product is processed, the observed value being relative to the at least one device, the at least one device including the at least one control parameter; and controlling the at least one control parameter includes affecting the at least one device.

According to one or more embodiments of the invention, the process includes a plurality of devices including a first device and a second device on which the plurality of products including the at least one product are processed, the observed value being relative to the first device, the second device including the at least one control parameter; and controlling the at least one control parameter includes affecting at least the second device.

According to one or more embodiments of the present invention, the first portion and second portion are determined over two or more products.

According to one or more embodiments of the invention, the first portion and second portion of the variance are calculated using an autoregressive stochastic sequence.

According to one or more embodiments of the invention, the first portion and second portion of the variance are determined by:
δx =y/(1+z)

    • where
      • δx=a value representing the variation that a system can control
      • δw=a value representing the random variation that is not controlled

According to one or more embodiments of the invention, the white noise includes at least one of random variance normal deviation, and an ambient fluctuation. Further, according to one or more embodiments of the invention, the first portion and second portion of the variance are calculated using an autoregressive stochastic sequence.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The above mentioned and other features and advantages of the present invention will become more readily apparent from the following detailed description and the accompanying drawings, in which:

FIG. 1 is an example block diagram of recursive parameter estimation for use in connection with one or more embodiments of the present invention.

FIG. 2 is a block diagram of an example of the recursive parameter estimation, utilizing the invention illustrated in FIG. 1.

FIG. 3 is a block diagram of recursive parameter estimation, utilizing one or more embodiments of the present invention.

FIG. 4 is a flow chart of the recursive parameter estimation, utilizing the invention illustrated in FIG. 1.

FIG. 5 is a block diagram of a computerized process control system which may be used in connection with one or more embodiments of the present invention.

FIG. 6 is an illustration of a computer appropriate for use in connection with one or more embodiments of the present invention.

FIG. 7 is a block diagram illustrating the internal hardware of FIG. 6.

DETAILED DESCRIPTION OF THE INVENTION

The following detailed description includes many specific details. The inclusion of such details is for the purpose of illustration only and should not be understood to limit the invention. Throughout this discussion, similar elements are referred to by similar numbers in the various figures for ease of reference. In addition, features in one embodiment may be combined with features in other embodiments of the invention.

Differentiating the uncontrollable white noise component generated by a processing device from the systematic component, such as controllable errors generated or experienced by the device, assists in identifying an area in which additional control and/or more precise control may be applied within a process control system.

As contemplated by one or more embodiments of the present invention, the value of the white noise component may be extracted from the statistical process variance, such as by utilizing an autoregressive stochastic sequence (such as the one described below). This permits tighter control, which according to one or more embodiments of the present invention is helpful in determining a dynamic threshold for tighter feedback and/or feed forward control, while taking into consideration the real time run-to-run conditions relating to the process, material, and/or processing device.

An nth order autoregressive stochastic sequence may be expressed as:
X k1 *X k−12 *X k−2+ . . . +ρn *X k−n +W k  (2)

    • where
      • Wk=white noise
      • ρn=auto-correlation coefficient at lag n
      • Xk=deviation of observed value from predicted value at run k

For the run-to-run control of the wafers:
X k1 *X k−1 +W k  (3)

To extract the white noise component, the variance analysis may be taken in two steps according to one or more embodiments of the invention. First,
V x1 2 *V x +V w  (4)

    • where
      • Vx=variance of the signal
      • Vw=variance of the white noise
      • w=white noise
      • x=signal

Next,
δwx=(1−ρ1 2)0.5  (5)

    • where δ=standard deviation.

Equation (5) provides an estimated gain adjustment, which is intended in effect to provide optimal weight factors taking into consideration a persistence of error conditions which may affect current conditions. The adjustment has cascaded functions of the above-described autoregressive stochastic sequence of equation (2).

From the above, it can be observed that:
Current parameter=previous parameter estimate+estimated gain*prediction error  (6)

Equations (7), (8) and (9) provide the estimated gain, weight factor and standard deviation derived from samples of the previous wafers.
Estimated Gain weight factor*Gain  (7)
Weight factor=f1n)  (8)

    • where δ=standard deviation from N samples of previous wafers
      δNxw  (9)

From equation (5), the following can be derived:
δN=y  (10)

    • where y=calculated value from N samples of the previous wafers and
      δwx =z  (11)
    • where z=calculated value from equation (5)

From equations (9), (10) and (11), it follows that:
δx =y/(1+z)  (12)

    • where
      • δx=a value representing the variation that a system can control (i.e., the systematic component)
      • δw=a value representing the random variation that is relatively non-controllable (i.e., the white noise component)

Wafers that have been processed in a sequence (run-to-run) by a given device or system typically have a relationship to each other in terms of a variance that they create. In summary, equation (3) is one example of taking into consideration the run-to-run control of wafers; it addresses information representative of the sequence and the relationship between the wafers and the sequence. Equation (4) follows from equations (2) and (3). Equation (5) concerns the ratio between the white noise component and the observed value. Based on the ratio between the white noise component and the observed value, it can be determined what the total amount of error is. By utilizing feed forward analysis, the process in which the error is observed can be appropriately controlled. Estimated gain factors are utilized, as shown in equations (7) and (8). Referring to equation (9), the observed value now has been differentiated into two components, one of which is the white noise component and the other of which is real deviation, also referred to as the systematic component. Equations (10) and (11) follow from the foregoing.

Consider for instance that a CMP processing device will be used in a semiconductor process control system to polish twenty-five wafers. Xk is wafer twenty-four, Xk−1 is wafer twenty-three, etc. Because of the relationship and effect run-to-run wafers have on each other, the analysis used herein refers back to the previous wafer(s) (i.e., Xk−1, etc.) in determining how much control effectively may be applied to Xk, the wafer presently being processed.

Referring to FIG. 1, the measured values (or raw data from which these values can be generated) 107 for samples are obtained from, e.g., a processing device 106 and used by block 101 to determine an observed value and by the white noise estimator and update model parameter block 105. The estimator block 105 performs a white noise estimation, using, for example, the aforementioned auto-regressive stochastic sequence. Estimator block 105 then updates the model parameters (taking the white noise component into account), and produces the predicted value 108 as output, that is, what the measured value is expected to be. In contrast, the process block 101 merely produces an output indicative of the actual measured value 111. At block 103, the positive or negative difference between the predicted output 108 and the actual output 111 is determined. This difference is utilized as the predicted error 113, which is then input back into the estimator block 105 to be used as feedback in connection with the recursive parameter estimation. The estimator block 105 then utilizes the difference in the auto-regressive stochastic sequence (Such as equation (2)) to update the model. The updated model parameters 115 are provided to the processing device 106, e.g., a CMP processing device, a CVD device, an etch device, etc.

According to one or more embodiments of the present invention, the white noise component is differentiated on a real time basis from systematic errors that occur during a manufacturing process. Alternatively, according to other embodiments, the differentiation is performed, but not on a real-time basis. The ability to differentiate the white noise component from the systematic component during real-time may be particularly useful within any number of industries (such as the semiconductor industry) in connection with obtaining higher quality, increased throughput, lower cost of operation, and shorter ramp-up time. Further, one or more embodiments of the present invention may provide more accurate determinations of error and may lead to improved test and yield analysis. One or more embodiments of the present invention are suitable for use in process control systems concerned with, for example, wafer-to-wafer thickness control, uniformity control, within-wafer range control, defect analysis and/or control, etch critical dimension control, chemical vapor deposition thickness and/or uniformity control, physical vapor deposition uniformity and/or thickness control, metrology variations, and other process control, analysis, and/or measurement situations and processing devices.

The following Table 1 illustrates simulated data comparing the calculation of a threshold using standard deviation to the calculation of a threshold with an embodiment of the present invention. In Table 1, the number of minimum samples is N=10.

TABLE 1
Threshold Threshold
Sample Simulated with with the
Number Sample Value Standard Deviation Invention
1 3927
2 3893
3 3797
4 3713
5 3627
6 3602
7 3694
8 3732
9 3753
10 3781 45.0 14.1
11 3739 45.0 12.3
12 3694 45.0 9.2
13 3684 45.0 7.8
14 3673 45.0 7.8
15 3743 45.0 7.9
16 3753 45.0 5.4
17 3746 45.0 5.1
18 3710 45.0 5.3
19 3736 45.0 5.3

As shown in the above example simulated data, the threshold in a conventional process control system utilizing a threshold calculated using standard deviation is calculated to be at 45.0. In other words, the conventional standard deviation approach utilizes a fixed threshold or trigger. Where the observed values are within the threshold, the process control system does not attempt to control the error, whereas if the values are outside the threshold, the process control system does attempt to control the error. In contrast, the threshold calculated utilizing the invention is dynamic and varies, thereby presenting a sensitive threshold in real-time for feedback and feedforward control. The threshold varies from 14.1 down to 5.1 in the simulated data in Table 1. Hence, the present invention offers a more sensitive detection and/or control of “true” errors than the conventional process control system utilizing a fixed threshold.

Values within the threshold can be neglected since they cannot be effectively controlled. On the other hand, outside the threshold it is possible and useful to control the deviation. The reason that one ignores the value below the threshold is that this represents the calculated white noise component, which is not controllable. Once the threshold has been defined more accurately, as with the present invention, better control can be provided.

Reference is now made to FIG. 2, a block diagram illustrating use of one or more example embodiments of the present invention in connection with a chemical mechanical polishing (“CMP”) process. However, it is not intended to limit the invention to CMP, as it can be applied in connection with a number of processes, such as chemical vapor deposition (“CVD”), or etching or many other processes within the semiconductor industry. Moreover, the present invention may also be used with types of process control systems, in addition to the semiconductor industry, which are concerned with measurement of true error (i.e., error not including white noise) as discriminated from observed error.

In the CMP process example of FIG. 2, the process control system 215 controls the conventional controls of pressure, state, time, flow rate, etc., in connection with the wafers that are to be polished. At block 201, the polish process receives the input wafers together with the parameters 213 describing the polish process. At block 205, the estimator receives from the process control system, parameters 213 describing the target result for the CMP polish process, performs a white noise estimation using an auto-regressive, stochastic sequence, updates the model parameters, and outputs the predicted value 209. The polish process block 201 obtains, determines and outputs observed, actual measurements 207 of the wafers, after they have been polished. The difference between the actual measurements and the predicted value from the estimator block 205 is determined in block 203. Block 203 determines the error in the prediction 211, which is then further input into the estimator block 205, to be used as feedback in connection with the recursive parameter estimation, e.g., to avoid over-reacting to the noise which may be present in the observed, actual measurement. According to one or more embodiments of the invention, the noise is approximately extracted by the estimator block 205 utilizing the auto-regressive stochastic sequence discussed above. The updated model parameters 217 may be provided to the process control system 215, e.g., for further use.

As an example of another use of one or more embodiments of the present invention, consider that a specification is provided which requires the process control system to produce wafers of a certain thickness, among other things, so that a target thickness is achieved. In this particular processing sequence example, shown in FIG. 3, two processing devices 109, 111 are utilized in sequence, so that the second processing device can compensate for the first processing device (e.g., CMP). Referring to FIG. 3, the process block 301 outputs the observed measurements obtained from the first processing device 109, in order to ultimately be used for making feedforward adjustments to the second processing device 111. Those outputs are utilized in determining the variance, or predicted error, at block 103 of the observed values from the predicted values. The predicted error is then utilized to make adjustments to the second processing device 111, after being adjusted for the white noise component via the estimator and update model parameters block 305, preferably utilizing the autoregressive stochastic sequence discussed above. By so doing, the second processing device compensates for deficiencies and variances caused by the first processing device, such that according to this example, the wafers produced from the second processing device are within the range of specified target thickness.

In the above description, the measurement of the products is described as being taken in relation to a pre-determined specification. Alternatively, according to one or more embodiments, the measurements could be taken in relation to a real-time calculation based on prior measurements. In accordance with at least one or more embodiments, multiple specifications could be provided.

FIG. 4 is a flow chart representing one or more embodiments of the present invention. At step 401 the observed value for the product (or other measurement) is determined. At step 403, the predicted value is determined, including for example from a specification or from a prior measurement, possibly incorporating white noise estimation as indicated in FIG. 1. At step 405, the difference, or predicted error, between the observed value and the predicted value is obtained. At step 407, the white noise component of the observed error is calculated, using the current and prior observed errors, in an auto-regressive, stochastic sequence. At step 409, it is determined whether the non-white noise component of the observed error is controllable, the system may adjust a control parameter 411. (For example, a gas pressure or temperature of the production environment might be adjusted, based on the systematic component.) At step 413, the system gets the next product to be measured, or obtains the next measurement.

Also, as described above, the difference between the measurements of the product, such as a wafer, and the specification for that product is calculated. However, the process control system may also calculate a statistical variance between such measurements and the specification. As described above, the specification could be pre-determined or could be based on a real-time calculation, such as determined by prior measurements.

From the difference or variance between observed values and predicted values, the process control system may, in effect, calculate two components, as explained above. The first component is indicative of that portion of the difference caused by random noise, random variance, and/or normal deviation. The second component determined by the system is indicative of the difference or variation caused by a systematic error or systematic variance over two or more products. Both the first and second components are calculated from a statistical variance.

If the actual variance exceeds a threshold, the process control system then may or may not attempt to control the process. For example, the process control system may adjust one or more control parameters that affect the process and that are relevant to the processing device, such as gas pressure, temperature, etc., thereby controlling the production environment. Hence, the process may be more tightly controlled, based upon the calculated second component of the differences or variance.

Other parameters that can be controlled in connection with semiconductor manufacturing include applying or changing electrical power, placing different gases into a chamber, changing distances between electrodes, etc. There are numerous variables in semi-conductor manufacturing that are to be measured and variances that may be controlled. The present invention is suitable for use in these situations.

This invention is described in connection with the semiconductor manufacturing industry. It can be used with other industries as well, particularly those that utilize measurements in an attempt to control variance in a process control system. The invention concerns determining, preferably but not necessarily in real-time, how much variance in a process control system can or cannot be controlled. According to one or more embodiments, the present invention determines how much of the standard deviation can be reduced and controlled.

The process control system used in connection with operation of one or more embodiments of the present invention may include a general purpose computer 501 as illustrated in FIG. 5, or a specially programmed special purpose computer. It may also be implemented as a distributed computer system rather than a single computer; some of the distributed systems might included embedded systems. Further, the programming may be distributed among one or more processing devices 503 themselves or other parts of the process control system. Similarly, the processing could be controlled by a software program on one or more computer systems or processors, or could be partially or wholly implemented in hardware. Moreover, the process control system may communicate directly or indirectly with the relevant processing devices and/or components thereof, such as metrology tools 505. As another alternative, the portion of the process control system that is relevant may be embedded in the processing device itself.

Deviation has been described herein as controlled above a certain threshold. In certain situations, it would be appropriate to consider deviation as controllable in an inverse manner. In other situations, deviation may have a range, above and below which control is possible.

Furthermore, it is possible that the invention could be used in connection with processes that are not part of a process control system, but that use multiple measurements and that are susceptible to white noise. One example is the financial industry, where fluctuations in values over time may be due to some uncontrollable influences, for which a dynamic and/or more precise threshold may be calculated and/or adjustments made.

FIG. 6 is an illustration of a computer 58 used for implementing the computer processing in accordance with a computer-implemented embodiment of the present invention. The procedures described above may be presented in terms of program procedures executed on, for example, a computer or network of computers.

Viewed externally in FIG. 6, computer 58 has a central processing unit (CPU) 68 having disk drives 69, 70. Disk drives 69, 70 are merely symbolic of a number of disk drives that might be accommodated by computer 58. Typically, these might be one or more of the following: a floppy disk drive 69, a hard disk drive (not shown), and a CD ROM or digital video disk, as indicated by the slot at 70. The number and type of drives varies, typically with different computer configurations. Disk drives 69, 70 are, in fact, options, and for space considerations, may be omitted from the computer system used in conjunction with the processes described herein.

Computer 58 also has a display 71 upon which information may be displayed. The display is optional for the computer used in conjunction with the system described herein. A keyboard 72 and/or a pointing device 73, such as a mouse 73, may be provided as input devices to interface with central processing unit 68. To increase input efficiency, keyboard 72 may be supplemented or replaced with a scanner, card reader, or other data input device. The pointing device 73 may be a mouse, touch pad control device, track ball device, or any other type of pointing device.

FIG. 7 illustrates a block diagram of the internal hardware of the computer of FIG. 6. CPU 75 is the central processing unit of the system, performing calculations and logic operations required to execute a program. Read only memory (ROM) 76 and random access memory (RAM) 77 constitute the main memory of the computer. Disk controller 78 interfaces one or more disk drives to the system bus 74. These disk drives may be floppy disk drives such as 79, or CD ROM or DVD (digital video/versatile disk) drives, as at 80, or internal or external hard drives 81. As previously indicated these various disk drives and disk controllers are optional devices.

A display interface 82 permits information from bus 74 to be displayed on the display 83. Again, as indicated, the display 83 is an optional accessory for a central or remote computer in the communication network, as are infrared receiver 88 and transmitter 89. Communication with external devices occurs using communications port 84.

In addition to the standard components of the computer, the computer may also include an interface 85, which allows for data input through the keyboard 86 or pointing device, such as a mouse 87.

As an example, the process control system may incorporate a factory automation system with a general purpose computer, or a specially programmed special purpose computer. It may also be implemented to include a distributed computer system rather than as a single computer; some of the distributed system might include embedded systems. Further, the programming may be distributed among processing devices and metrology tools and/or other parts of the process control system. Similarly, the processing could be controlled by a software program on one or more computer systems or processors, or could be partially or wholly implemented in hardware. Moreover, the factory automation system may communicate directly or indirectly with the relevant metrology tool(s), processing device(s), and metrology system(s); or the metrology tool(s), processing device(s) and metrology system(s) may communicate directly or indirectly with each other and the factory automation system.

As another example, the system may be implemented on a web based computer, e.g., via an interface to collect and/or analyze data from many sources. It may be connected over a network, e.g., the Internet, an Intranet, or even on a single computer system. Moreover, portions of the system may be distributed (or not) over one or more computers, and some functions may be distributed to other hardware, such as tools, and still remain within the scope of this invention. The user may interact with the system via e.g., a personal computer or over PDA, e.g., the Internet, an intranet, etc. Either of these may be implemented as a distributed computer system rather than a single computer. Similarly, a communications link may be a dedicated link, a modem over a POTS line, and/or any other method of communicating between computers and/or users. Moreover, the processing could be controlled by a software program on one or more computer systems or processors, or could even be partially or wholly implemented in hardware.

User interfaces may be developed in connection with an HTML display format. It is possible to utilize alternative technology for displaying information, obtaining user instructions and for providing user interfaces.

The invention has been discussed in connection with particular examples. However, the principles apply equally to other examples and/or realizations. For example, particular semi-conductor processes such as chemical vapor deposition were discussed, although the invention may be performed in connection with other semi-conductor processes. In addition, one or more embodiments of the invention are useful with systems not related to semi-conductor, in which processing adjustments are made and processing may benefit from distinguishing white noise. Such systems include, by way of example, systems for producing optical lenses, systems for controlling room temperature, and/or systems for controlling water temperature.

The system used in connection with the invention may rely on the integration of various components including, as appropriate and/or if desired, hardware and software servers, database engines, and/or other process control components. The configuration may be, alternatively, network-based and may, if desired, use the Internet as an interface with the user.

The system according to one or more embodiments of the invention may store collected information in a database. An appropriate database may be on a standard server, for example, a small Sun™ Sparc™ or other remote location. The information may, for example, optionally be stored on a platform that may, for example, be UNIX-based. The various databases maybe in, for example, a UNIX format, but other standard data formats may be used.

Although the process control system is illustrated as having a single computer, the system according to one or more embodiments of the invention is optionally suitably equipped with a multitude or combination of processors or storage devices. For example, the computer may be replaced by, or combined with, any suitable processing system operative in accordance with the principles of embodiments of the present invention, including sophisticated calculators, hand held, laptop/notebook, mini, mainframe and super computers, one or more embedded processors, as well as processing system network combinations of the same. Further, portions of the system may be provided in any appropriate electronic format, including, for example, provided over a communication line as electronic signals, provided on floppy disk, provided on CD Rom, provided on optical disk memory, etc.

Any presently available or future developed computer software language and/or hardware components can be employed in such embodiments of the present invention. For example, at least some of the functionality mentioned above could be implemented using Visual Basic, C, C++ or any assembly language appropriate in view of the processor being used. It could also be written in an interpretive environment such as Java and transported to multiple destinations to various users.

The invention may include a process and/or steps. Where steps are indicated, they may be performed in any order, unless expressly and necessarily limited to a particular order. Steps that are not so limited may be performed in any order.

While this invention has been described in conjunction with the specific embodiments outlined above, many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, the preferred embodiments of the invention as set forth are intended to be illustrative and not limiting. Various changes may be made without departing from the spirit and scope of the invention as defined in the application. The foregoing detailed description includes many specific details. The inclusion of such detail is for the purpose of illustration only and should not be understood to limit the invention. In addition, features in one embodiment may be combined with features in other embodiments of the invention. Various changes may be made without departing from the scope of the invention as defined in the application.

Referenced by
Citing PatentFiling datePublication dateApplicantTitle
US7221990 *Apr 6, 2006May 22, 2007Applied Materials, Inc.Process control by distinguishing a white noise component of a process variance
US8014590 *Dec 7, 2005Sep 6, 2011Drvision Technologies LlcMethod of directed pattern enhancement for flexible recognition
US8036758 *Apr 7, 2008Oct 11, 2011Honeywell International Inc.System and method for continuous supply chain control and optimization using stochastic calculus of variations approach
US8374839 *Dec 7, 2009Feb 12, 2013Jedat Inc.System for creating parameter information, system for estimating yields, program and recording medium
US20100153086 *Dec 7, 2009Jun 17, 2010Jedat Inc.System for creating parameter information, system for estimating yields, program and recording medium
US20120203370 *Oct 19, 2010Aug 9, 2012Takashi KawasakiManufacturing method and manufacturing system for product
Classifications
U.S. Classification700/108, 700/121
International ClassificationG06F19/00, G05B13/04
Cooperative ClassificationG05B13/041
European ClassificationG05B13/04A
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